The ideal candidate should possess a minimum of 10 years of hands-on experience working with Large Language Models (LLM), Computer Vision, and various open-source machine learning models. A strong foundation in system engineering is essential, and familiarity with Model-Based Systems Engineering (MBSE) would be a significant advantage. The AI Lead will be responsible for guiding the teams technical direction, ensuring successful project outcomes, and driving innovation in the application of AI technologies.

Responsibilities

Lead and manage a team of data scientists and data analysts to design, develop, and deploy AI-driven solutions for the aviation industry.
Collaborate closely with cross-functional teams including engineers, domain experts, and product managers to define project goals, scope, and technical requirements.
Provide technical expertise and leadership in the selection, implementation, and optimization of machine learning models and techniques, with a strong focus on LLM, Computer Vision, and open-source frameworks.
Drive the development of AI models and algorithms to solve complex aviation challenges, such as predictive maintenance, anomaly detection, and optimization of operational processes.
Apply a deep understanding of system engineering principles to ensure AI solutions integrate seamlessly within aviation systems and adhere to safety and regulatory standards.
Mentor and guide team members, fostering a culture of continuous learning and technical excellence.
Stay up-to-date with the latest advancements in AI and machine learning, and identify opportunities to apply emerging technologies to enhance our products and services.
Collaborate with stakeholders to communicate project progress, technical insights, and potential risks in a clear and concise manner.

Requirements:

Minimum of 8+ years of hands-on experience in the field of AI, with a strong focus on LLM, Computer Vision, and open-source machine learning models.
Proven track record of leading and managing teams in the successful development and deployment of AI products.
In-depth knowledge of system engineering principles, with the ability to apply them to complex projects within the aviation domain.
Familiarity with Model-Based Systems Engineering (MBSE) concepts is a significant advantage.
Extensive experience in designing, training, and optimizing AI models using a variety of machine learning techniques and frameworks.
Strong programming skills in languages such as Python, and proficiency in relevant libraries and tools (e.g., SageMaker, TensorFlow, PyTorch, scikit-learn).
Excellent problem-solving abilities, critical thinking, and a data-driven approach to decision-making.
Effective communication skills to collaborate with cross-functional teams and convey technical concepts to non-technical stakeholders.
Proven ability to stay updated with the latest advancements in AI and machine learning research.
A masters or doctoral degree in a relevant field (e.g., Computer Science, Electrical Engineering, Aerospace Engineering) is preferred.

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